چكيده به لاتين
In this thesis, first the boiler-turbine and its structure are introduced and then the control
methods of the MPC and NMPC are applied on it. In order to improve the performance of the
controller, since the neural networks are general approximators and can model any Lipschitz
nonlinear systems using input-output data of the system, neural network model is utilized as the
predictor model. Adaptive models can consider the system changes in each time step, therefore
the adaptive neural network model is used and this helps to achieve tracking. The main
objectives of this thesis are to trace the reference signal, reduce the effect of disturbances and
noise using the expressed controllers. The intended disturbances are divided into two
categories: internal disturbances caused by changes in system parameters (uncertainty), and
external disturbances. To guarantee the closed-loop stability in the presence of disturbances, a
constraint tightening approach based on the bounds of the disturbances and the Lipschitz
constant of the adaptive model is proposed. This approach guarantees the Input-to-State
Stability (ISS) of the closed-loop system in the presence of the step disturbances. To eliminate
the effect of long time disturbances, a hybrid predictive control with feed-forward control based
on disturbance observer is proposed. The important feature of this structure is that the design
of predictive control for reference tracking can perform independent of the design of
feedforward control for disturbance rejection.
The effectiveness of the proposed methods is evaluated in the simulation and experimental
studies and is compared with the recently reported methods in literature.
